An LLM prompted to “implement SQLite in Rust” will generate code that looks like an implementation of SQLite in Rust. It will have the right module structure and function names. But it can not magically generate the performance invariants that exist because someone profiled a real workload and found the bottleneck. The Mercury benchmark (NeurIPS 2024) confirmed this empirically: leading code LLMs achieve ~65% on correctness but under 50% when efficiency is also required.
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2026-03-04 00:00:00:03014333810http://paper.people.com.cn/rmrb/pc/content/202603/04/content_30143338.htmlhttp://paper.people.com.cn/rmrb/pad/content/202603/04/content_30143338.html11921 一版责编：杨 旭 胡安琪 张帅祯 二版责编：殷新宇 张安宇 何 彪 三版责编：吴 刚 周 輖 程是颉 四版责编：袁振喜 刘 念 刘静文，这一点在体育直播中也有详细论述
2025年10月，他在社交媒体上透露，已组建机器人和具身智能的小型团队。